Background

This suite of tutorials was developed for a workshop at the 2021 R-Medicine Conference by the Healthy Regions & Policies Lab at the University of Chicago. This workbook is a quick (3-hour) overview of mapping, GIScience, and spatial analysis basics for health professionals. The workbook was compiled by Marynia Kolak, and the overview for each section is led by Susan Paykin in the live version.

Some coding snippets & data examples are from the phenomenal team of the Opioid Environment Toolkit (Moksha Menghaney, Qinyun Lin, Angela Li). The overall approach follows the Center for Spatial Data Science paradigm, led by Luc Anselin & Julia Koschinsky.

Environment Setup

A basic understanding of R is assumed. This workshop requires several packages, which can be installed from CRAN:

For Mac users, check out https://github.com/r-spatial/sf for additional tips if you run into errors when installing the sf package. Using homebrew to install gdal usually fixes any remaining issues.

1 Intro to Spatial Data

In the workshop, we learned about:

  • What is Spatial Data?
  • What is the sf framework for R?

To delve in further, let’s see some spatial data in action.

We’ll work with the sf library first.

1.1 Load Spatial Data

First load in the shapefile. Remember, this type of data is actually comprised of multiple files. All need to be present in order to read correctly.

## Reading layer `geo_export_aae47441-adab-4aca-8cb0-2e0c0114096e' from data source 
##   `/Users/maryniakolak/code/Intro2RSpatialMed/data/geo_export_aae47441-adab-4aca-8cb0-2e0c0114096e.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 801 features and 9 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.94025 ymin: 41.64429 xmax: -87.52366 ymax: 42.02392
## CRS:           4326

1.2 Non-Spatial & Spatial Views

Always inspect data when loading in. First we look at a non-spatial view.

## Simple feature collection with 6 features and 9 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.68822 ymin: 41.72902 xmax: -87.62394 ymax: 41.87455
## CRS:           4326
##   commarea commarea_n countyfp10     geoid10 name10        namelsad10
## 1       44         44        031 17031842400   8424 Census Tract 8424
## 2       59         59        031 17031840300   8403 Census Tract 8403
## 3       34         34        031 17031841100   8411 Census Tract 8411
## 4       31         31        031 17031841200   8412 Census Tract 8412
## 5       32         32        031 17031839000   8390 Census Tract 8390
## 6       28         28        031 17031838200   8382 Census Tract 8382
##   notes statefp10 tractce10                       geometry
## 1  <NA>        17    842400 POLYGON ((-87.62405 41.7302...
## 2  <NA>        17    840300 POLYGON ((-87.68608 41.8229...
## 3  <NA>        17    841100 POLYGON ((-87.62935 41.8528...
## 4  <NA>        17    841200 POLYGON ((-87.68813 41.8556...
## 5  <NA>        17    839000 POLYGON ((-87.63312 41.8744...
## 6  <NA>        17    838200 POLYGON ((-87.66782 41.8741...

Note the last column – this is a spatially enabled column. The data is no longer a ‘shapefile’ but an `sf’ object, comprised of polygons.

We can use a baseR function to view the spatial dimension. The sf framework enables previews of each attribute in our spatial file.

1.3 Spatial Data Structure

Check out the data structure of this file… What object is it?

## Classes 'sf' and 'data.frame':   801 obs. of  10 variables:
##  $ commarea  : Factor w/ 77 levels "1","10","11",..: 39 55 28 25 26 21 62 49 74 75 ...
##  $ commarea_n: num  44 59 34 31 32 28 65 53 76 77 ...
##  $ countyfp10: Factor w/ 1 level "031": 1 1 1 1 1 1 1 1 1 1 ...
##  $ geoid10   : Factor w/ 801 levels "17031010100",..: 785 767 772 773 756 751 584 513 684 34 ...
##  $ name10    : Factor w/ 801 levels "1001","1002",..: 782 764 769 770 753 748 545 443 663 266 ...
##  $ namelsad10: Factor w/ 801 levels "Census Tract 1001",..: 782 764 769 770 753 748 545 443 663 266 ...
##  $ notes     : Factor w/ 7 levels "Half in CA 64 (Midway Airport)",..: NA NA NA NA NA NA NA NA NA NA ...
##  $ statefp10 : Factor w/ 1 level "17": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tractce10 : Factor w/ 801 levels "010100","010201",..: 785 767 772 773 756 751 584 513 684 34 ...
##  $ geometry  :sfc_POLYGON of length 801; first list element: List of 1
##   ..$ : num [1:243, 1:2] -87.6 -87.6 -87.6 -87.6 -87.6 ...
##   ..- attr(*, "class")= chr  "XY" "POLYGON" "sfg"
##  - attr(*, "sf_column")= chr "geometry"
##  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA
##   ..- attr(*, "names")= chr  "commarea" "commarea_n" "countyfp10" "geoid10" ...

Check out the coordinate reference system. What is it? What are the units?

## Coordinate Reference System:
##   User input: 4326 
##   wkt:
## GEOGCS["WGS84(DD)",
##     DATUM["WGS84",
##         SPHEROID["WGS84",6378137.0,298.257223563]],
##     PRIMEM["Greenwich",0.0],
##     UNIT["degree",0.017453292519943295],
##     AXIS["Geodetic longitude",EAST],
##     AXIS["Geodetic latitude",NORTH]]

1.4 Exploring Coordinate Reference Systems

Lets see how switching CRS changes our object. First we’ll try the Mollweide coordinate reference system that does a good job preserving area across the globe.

To transform our CRS, we use the st_transform function. To plot, we use baseR again but with some paremeter updates. Finally, we check out the CRS of our new object. What are the units? Any other details to note? Will this be appropriate for our spatial analysis?

## Coordinate Reference System:
##   User input: +proj=moll 
##   wkt:
## PROJCS["unnamed",
##     GEOGCS["WGS 84",
##         DATUM["unknown",
##             SPHEROID["WGS84",6378137,298.257223563]],
##         PRIMEM["Greenwich",0],
##         UNIT["degree",0.0174532925199433]],
##     PROJECTION["Mollweide"],
##     PARAMETER["central_meridian",0],
##     PARAMETER["false_easting",0],
##     PARAMETER["false_northing",0]]

Next, we’ll try the Winkel CRS, which is a compromise projection that facilitates minimal distortion for area, distance, and angles. We use the same approach, recyling the code with new inputs.

## Coordinate Reference System:
##   User input: EPSG:54019 
##   wkt:
## PROJCS["World_Winkel_II",
##     GEOGCS["GCS_WGS_1984",
##         DATUM["WGS_1984",
##             SPHEROID["WGS_84",6378137.0,298.257223563]],
##         PRIMEM["Greenwich",0.0],
##         UNIT["Degree",0.0174532925199433]],
##     PROJECTION["Winkel_II"],
##     PARAMETER["False_Easting",0.0],
##     PARAMETER["False_Northing",0.0],
##     PARAMETER["Central_Meridian",0.0],
##     PARAMETER["Standard_Parallel_1",50.45977625218981],
##     UNIT["Meter",1.0],
##     AUTHORITY["Esri","54019"]]

We could also try a totally different projection, to see how that changes our spatial object. Let’s use the “Old Hawaiian UTM Zone 4n” projection, with the EPSG identified from an online search. How does this fare?

Finally.. let’s choose a projection that is focused on Illinois, and uses distance as feet or meters, to make it a bit more accessible for our work. EPSG:3435 is a good fit:

## Coordinate Reference System:
##   User input: EPSG:3435 
##   wkt:
## PROJCS["NAD83 / Illinois East (ftUS)",
##     GEOGCS["NAD83",
##         DATUM["North_American_Datum_1983",
##             SPHEROID["GRS 1980",6378137,298.257222101,
##                 AUTHORITY["EPSG","7019"]],
##             TOWGS84[0,0,0,0,0,0,0],
##             AUTHORITY["EPSG","6269"]],
##         PRIMEM["Greenwich",0,
##             AUTHORITY["EPSG","8901"]],
##         UNIT["degree",0.0174532925199433,
##             AUTHORITY["EPSG","9122"]],
##         AUTHORITY["EPSG","4269"]],
##     PROJECTION["Transverse_Mercator"],
##     PARAMETER["latitude_of_origin",36.66666666666666],
##     PARAMETER["central_meridian",-88.33333333333333],
##     PARAMETER["scale_factor",0.999975],
##     PARAMETER["false_easting",984250.0000000002],
##     PARAMETER["false_northing",0],
##     UNIT["US survey foot",0.3048006096012192,
##         AUTHORITY["EPSG","9003"]],
##     AXIS["X",EAST],
##     AXIS["Y",NORTH],
##     AUTHORITY["EPSG","3435"]]

1.5 Refine Basic Map

Now we’ll switch to a more extensive cartographic mapping package, tmap. We approach mapping with one layer at a time. Always start with the object you want to map by calling it with the tm_shape function. Then, at least one descriptive/styling function follows. There are hundreds of variations and paramater specifications, so take your time in exploring tmap and the options.

Here we style the tracts with some semi-transparent borders.

Next we fill the tracts with a light gray, and adjust the color and transparency of borders. We also add a scale bar, positioning it to the left and having a thickness of 0.8 units, and turn off the frame.

Check out https://rdrr.io/cran/tmap/man/tm_polygons.html for more ideas!

1.6 Arrange multiple maps

Sometimes we want to look at multiple maps at once. Write your mapping function to a new variable, and then call that variable in order of desire using the tmap_arrange function. Hint: this is just one of many! ways to map multiples using tmap… see if you can uncover more in the documentation.

1.7 Interactive Mode

So far, we’ve been plotting static maps. We can also switch to an interactive map that uses a Leaflet widget by switching the tmap_mode() parameter specification from “plot” to “view.” It’s on “plot” as default.

## tmap mode set to interactive viewing

Map the same map as before, and check out the interaction!

The tracts are not transparent enough, so we update that here. You can also click the box on the left side to try out other basemaps. See if you can find out how to add a basemap to a static/plotted map, using tmap documentation…

We revert back to plot mode for now.

## tmap mode set to plotting

1.8 Overlay Zip Code Boundaries

How do census tract areas correspond to zip codes? While tracts better represent neighborhoods, often times we are stuck with zip code level scale in healh research. Here we’ll make a reference map to highlight tract distribution across each zip code.

First, we read in zip code boundaries. This data was downloaded directly from the City of Chicago Data Portal as a shapefile.

## Reading layer `geo_export_54bc15d8-5ef5-40e4-8f72-bb0c6dbac9a5' from data source 
##   `/Users/maryniakolak/code/Intro2RSpatialMed/data/geo_export_54bc15d8-5ef5-40e4-8f72-bb0c6dbac9a5.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 61 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.94011 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
## CRS:           4326

Next, we layer the new shape in – on top of the tracts. We use a thicker border, and try out a new color. Experiment!

2 Map Neighborhoods

When considering the health of persons, we have to also consider the neighborhood environment. Sometimes this is looking at neighborhood level health outcomes, like premature mortality at the census tract scale, or cumulative COVID rates by zip code. Sometimes we’re interested in neighborhood factors like poverty, access to affordable housing, or distance to nearest health provider, or pollution-emitting facility. These measurements of the “social determinants of health” at the neighborhood scale are increasingly urgent in modern public health thinking, and are thought to drive and/or reinforce racial, social, and spatial inequity

In this module, we’ll learn about the basics of thematic mapping – known as choropleth mapping – to visualize neighborhood level health phenomena. This will allow you to begin the process of exploratory spatial data analysis and hypothesis generation & refinement.

2.1 Clean Attribute Data

Let’s consider COVID-19 cases by zip code in Chicago. We’ll upload and inspect a summary of cases from the Chicago Data Portal first:

##   ZIP.Code Week.Number Week.Start   Week.End Cases...Weekly
## 1    60603          39 09/20/2020 09/26/2020              0
## 2    60604          39 09/20/2020 09/26/2020              0
## 3    60611          16 04/12/2020 04/18/2020              8
## 4    60611          15 04/05/2020 04/11/2020              7
## 5    60615          11 03/08/2020 03/14/2020             NA
## 6    60603          10 03/01/2020 03/07/2020             NA
##   Cases...Cumulative Case.Rate...Weekly Case.Rate...Cumulative
## 1                 13                  0                 1107.3
## 2                 31                  0                 3964.2
## 3                 72                 25                  222.0
## 4                 64                 22                  197.4
## 5                 NA                 NA                     NA
## 6                 NA                 NA                     NA
##   Tests...Weekly Tests...Cumulative Test.Rate...Weekly
## 1             25                327               2130
## 2             12                339               1534
## 3            101                450                312
## 4             59                349                182
## 5              6                  9                 14
## 6              0                  0                  0
##   Test.Rate...Cumulative Percent.Tested.Positive...Weekly
## 1                27853.5                              0.0
## 2                43350.4                              0.0
## 3                 1387.8                              0.1
## 4                 1076.3                              0.1
## 5                   21.7                               NA
## 6                    0.0                               NA
##   Percent.Tested.Positive...Cumulative Deaths...Weekly
## 1                                  0.0               0
## 2                                  0.1               0
## 3                                  0.2               0
## 4                                  0.2               0
## 5                                   NA               0
## 6                                   NA               0
##   Deaths...Cumulative Death.Rate...Weekly Death.Rate...Cumulative
## 1                   0                   0                       0
## 2                   0                   0                       0
## 3                   0                   0                       0
## 4                   0                   0                       0
## 5                   0                   0                       0
## 6                   0                   0                       0
##   Population   Row.ID            ZIP.Code.Location
## 1       1174 60603-39 POINT (-87.625473 41.880112)
## 2        782 60604-39 POINT (-87.629029 41.878153)
## 3      32426 60611-16 POINT (-87.620291 41.894734)
## 4      32426 60611-15 POINT (-87.620291 41.894734)
## 5      41563 60615-11 POINT (-87.602725 41.801993)
## 6       1174 60603-10 POINT (-87.625473 41.880112)

Each row corresponds to a zip code at a different week. This data thus exists as a “long” format, which doesn’t work for spatial analysis. We need to convert to “wide” format, or at the very least, ensure that each zip code corresponds to one row.

To simplify, let’s identify the last week of the dataset, and then subset the data frame to only show that week. We will be interested in the cumulative case rate. Following is one way of doing this – can you think of another way? Try out different approaches of reshaping data to test your R and “tidy” skills.

## [1]  1 31
##    ZIP.Code Week.Number Week.Start   Week.End Cases...Weekly
## 1     60603          39 09/20/2020 09/26/2020              0
## 2     60604          39 09/20/2020 09/26/2020              0
## 36    60601          39 09/20/2020 09/26/2020              8
## 37    60602          39 09/20/2020 09/26/2020              0
## 41    60605          39 09/20/2020 09/26/2020             12
## 66    60610          39 09/20/2020 09/26/2020             35
##    Cases...Cumulative Case.Rate...Weekly Case.Rate...Cumulative
## 1                  13                  0                 1107.3
## 2                  31                  0                 3964.2
## 36                213                 54                 1451.4
## 37                 21                  0                 1688.1
## 41                391                 44                 1420.8
## 66                666                 90                 1706.9
##    Tests...Weekly Tests...Cumulative Test.Rate...Weekly
## 1              25                327               2130
## 2              12                339               1534
## 36            202               4304               1376
## 37             27                460               2170
## 41            291               7160               1058
## 66            500              10680               1281
##    Test.Rate...Cumulative Percent.Tested.Positive...Weekly
## 1                 27853.5                              0.0
## 2                 43350.4                              0.0
## 36                29328.8                              0.0
## 37                36977.5                              0.0
## 41                26018.4                              0.0
## 66                27371.3                              0.1
##    Percent.Tested.Positive...Cumulative Deaths...Weekly
## 1                                   0.0               0
## 2                                   0.1               0
## 36                                  0.0               1
## 37                                  0.0               0
## 41                                  0.1               1
## 66                                  0.1               0
##    Deaths...Cumulative Death.Rate...Weekly Death.Rate...Cumulative
## 1                    0                 0.0                     0.0
## 2                    0                 0.0                     0.0
## 36                   6                 6.8                    40.9
## 37                   0                 0.0                     0.0
## 41                   3                 3.6                    10.9
## 66                  10                 0.0                    25.6
##    Population   Row.ID            ZIP.Code.Location
## 1        1174 60603-39 POINT (-87.625473 41.880112)
## 2         782 60604-39 POINT (-87.629029 41.878153)
## 36      14675 60601-39 POINT (-87.622844 41.886262)
## 37       1244 60602-39 POINT (-87.628309 41.883136)
## 41      27519 60605-39 POINT (-87.623449 41.867824)
## 66      39019 60610-39   POINT (-87.63581 41.90455)

To clean our data a bit, we’ll just keep the zip code name, and cumulative case rate for the week of September 20th, 2020.

##    ZIP.Code Case.Rate...Cumulative
## 1     60603                 1107.3
## 2     60604                 3964.2
## 36    60601                 1451.4
## 37    60602                 1688.1
## 41    60605                 1420.8
## 66    60610                 1706.9

2.2 Merge Spatial Data

Next, let’s merge this data to our zip code master spatial file. Reload if necessary:

## Reading layer `geo_export_54bc15d8-5ef5-40e4-8f72-bb0c6dbac9a5' from data source 
##   `/Users/maryniakolak/code/Intro2RSpatialMed/data/geo_export_54bc15d8-5ef5-40e4-8f72-bb0c6dbac9a5.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 61 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.94011 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
## CRS:           4326
## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.80649 ymin: 41.88747 xmax: -87.59852 ymax: 41.93228
## CRS:           4326
##   objectid shape_area shape_len   zip                       geometry
## 1       33  106052287  42720.04 60647 MULTIPOLYGON (((-87.67762 4...
## 2       34  127476051  48103.78 60639 MULTIPOLYGON (((-87.72683 4...
## 3       35   45069038  27288.61 60707 MULTIPOLYGON (((-87.785 41....
## 4       36   70853834  42527.99 60622 MULTIPOLYGON (((-87.66707 4...
## 5       37   99039621  47970.14 60651 MULTIPOLYGON (((-87.70656 4...
## 6       38   23506056  34689.35 60611 MULTIPOLYGON (((-87.61401 4...

Next, merge on zip code ID. The key in the Chi_Zips object is zip, whereas the key for the COVID data is ZIP.code. Always merge non-spatial to spatial data, not the other way around. Think of the spatial file as your master file that you will continue to add on to…

## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.63999 ymin: 41.85317 xmax: -87.60246 ymax: 41.88913
## CRS:           4326
##     zip objectid shape_area shape_len Case.Rate...Cumulative
## 1 60601       27    9166246  19804.58                 1451.4
## 2 60602       26    4847125  14448.17                 1688.1
## 3 60603       19    4560229  13672.68                 1107.3
## 4 60604       48    4294902  12245.81                 3964.2
## 5 60605       20   36301276  37973.35                 1420.8
## 6 60606       31    6766411  12040.44                 2289.6
##                         geometry
## 1 MULTIPOLYGON (((-87.62271 4...
## 2 MULTIPOLYGON (((-87.60997 4...
## 3 MULTIPOLYGON (((-87.61633 4...
## 4 MULTIPOLYGON (((-87.63376 4...
## 5 MULTIPOLYGON (((-87.62064 4...
## 6 MULTIPOLYGON (((-87.63397 4...

2.3 Quantile Maps

Starting with a “classic epi” approach, let’s look at case rates as quantiles. We use the tmap library, and update the choropleth data classification using the style parameter. We use the Blue-Purple palette, or BuPu, from Colorbrewer.

Colorbrewer Tip: To display all Colorbrewer palette options, load the RColorBrewer library and run display.brewer.all() – or just Google “R Colorbrewer palettes.”

Let’s try tertiles:

2.4 Standard Deviation Maps

While quantiles are a nice start, let’s classify using a standard deviation map. Standard deviation is a statistical technique type of map based on how much the data differs from the mean.

2.5 Jenks Maps

Another approach of data classification is natural breaks, or jenks. This approach looks for “natural breaks” in the data using a univariate clustering algorithm.

The first bin doesn’t seem very intuitive. Let’s try 4 bins instead of 5 by changing the n parameter. In this version, we’ll also had a histogram and scale bar, and move the legend outside the frame to make it easier to view.

2.6 Integrate More Data

To explore potential disparities in COVID health outcomes, let’s bring in pre-cleaned demographic, racial, and ethnic data from the Opioid Environment Policy Scan database. This data is orginally sourced from the American Community Survey 2018 5-year estimate, which you could also pull using the tidycensus.

##    ZCTA year totPopE whiteP blackP amIndP asianP pacIsP otherP  hispP
## 1 35004 2018   11762  84.39  13.09   0.00   0.94   0.00   1.57   0.94
## 2 35005 2018    7528  55.22  42.44   0.64   0.00   0.15   1.55   1.37
## 3 35006 2018    2927  96.04   3.21   0.27   0.00   0.00   0.48   0.00
## 4 35007 2018   26328  73.83  13.75   0.04   1.33   0.02  11.01  11.11
## 5 35010 2018   20625  63.07  32.43   0.39   0.65   0.00   3.45   4.10
## 6 35013 2018      40 100.00   0.00   0.00   0.00   0.00   0.00 100.00
##    noHSP age0_4 age5_14 age15_19 age20_24 age15_44 age45_49 age50_54
## 1   5.52    787    1950      457      746     4552      662      541
## 2  17.48    511    1055      455      277     2429      580      469
## 3  14.44    161     413      141      203      878      129      193
## 4  12.41   1891    4161     1619     1400     9947     1993     2067
## 5  22.00   1013    2647     1383     1087     7036     1418     1545
## 6 100.00      0       0        0        0       13        8       19
##   age55_59 age60_64 ageOv65 ageOv18 age18_64 a15_24P und45P ovr65P
## 1      776      832    1662    8820     7158   10.23  61.97  14.13
## 2      560      552    1372    5691     4319    9.72  53.07  18.23
## 3      316      278     559    2308     1749   11.75  49.61  19.10
## 4     1713     1315    3241   19178    15937   11.47  60.77  12.31
## 5     1510     1341    4115   16142    12027   11.98  51.86  19.95
## 6        0        0       0      40       40    0.00  32.50   0.00
##   disbP
## 1  12.7
## 2  23.2
## 3  20.9
## 4  13.5
## 5  19.6
## 6   0.0

Merge to our master Zip Code dataset.

## Simple feature collection with 6 features and 31 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.63999 ymin: 41.85317 xmax: -87.60246 ymax: 41.88913
## CRS:           4326
##     zip objectid shape_area shape_len Case.Rate...Cumulative year
## 1 60601       27    9166246  19804.58                 1451.4 2018
## 2 60602       26    4847125  14448.17                 1688.1 2018
## 3 60603       19    4560229  13672.68                 1107.3 2018
## 4 60604       48    4294902  12245.81                 3964.2 2018
## 5 60605       20   36301276  37973.35                 1420.8 2018
## 6 60606       31    6766411  12040.44                 2289.6 2018
##   totPopE whiteP blackP amIndP asianP pacIsP otherP hispP noHSP age0_4
## 1   14675  74.17   5.57   0.45  18.00   0.00   1.81  8.68  0.00    550
## 2    1244  68.17   3.78   5.31  19.45   0.00   3.30  6.51  0.00     61
## 3    1174  63.46   3.24   0.00  27.60   0.00   5.71  9.80  0.00     13
## 4     782  63.43   5.63   0.00  29.67   0.00   1.28  4.35  0.00     12
## 5   27519  61.20  17.18   0.18  16.10   0.03   5.31  5.84  2.39    837
## 6    3101  72.75   2.35   0.00  18.09   0.00   6.80  6.29  0.73     57
##   age5_14 age15_19 age20_24 age15_44 age45_49 age50_54 age55_59
## 1     156      907      909     8726      976     1009      324
## 2      87       18       91      987       46       53        0
## 3      43      179      172      684       75       47      150
## 4       7       52      168      450       27       47       54
## 5    1279     2172     2282    16364     1766     1520     1824
## 6      44        0      139     1863      213      153      168
##   age60_64 ageOv65 ageOv18 age18_64 a15_24P und45P ovr65P disbP
## 1      859    2075   13855    11780   12.37  64.27  14.14   6.4
## 2        5       5    1095     1090    8.76  91.24   0.40   0.2
## 3       50     112    1118     1006   29.90  63.03   9.54   7.3
## 4       92      93     744      651   28.13  59.97  11.89   4.1
## 5     1360    2569   25259    22690   16.19  67.15   9.34   5.3
## 6      172     431    3000     2569    4.48  63.33  13.90   1.9
##                         geometry
## 1 MULTIPOLYGON (((-87.62271 4...
## 2 MULTIPOLYGON (((-87.60997 4...
## 3 MULTIPOLYGON (((-87.61633 4...
## 4 MULTIPOLYGON (((-87.63376 4...
## 5 MULTIPOLYGON (((-87.62064 4...
## 6 MULTIPOLYGON (((-87.63397 4...

2.7 Thematic Map Panel

To facilitate data discovery, we likely want to explore multiple maps at once. Here we’ll generate maps for multiple variables, and plot them as a map panel.

Can you think of more efficient ways to run this code? There are also other tmap tricks to optimize this further, so enjoy your journey!

From the results, we see that cumulative COVID outcomes for one week in September 2020 seemed to have some geographic correlation with the Latinx/Hispanic community in Chicago. At the same time, low high school diploma rates are also concentrated in these areas, and there is some intersection with other variables considered. What are additional variables you could bring in to refine your approach? Perhaps percentage of essential workers; a different age group; internet access? What about linking in health outcomes like Asthma, Hypertension, and more at a similar scale?

In modern spatial epidemiology, associations must never be taken at face value. For example, we know that it is not “race” but “racism” that drives multiple health disparities – simply looking at a specific racial/ethnic group is not enough. Thus exploring multiple variables and nurturing a curiosity to understand these complex intersections will support knowledge discovery.

2.8 Write Data

We’re done! Well… not so fast. Let’s save the data so we don’t have to run the codebook again to access the data. Here, we’ll save as a geojson file. This spatial format is more forgiving with long column names, which is a long-standing challenge with shapefiles.

More Resources

For choropleth mapping in R:

3 Adding Resources

In addition to areal data, we can also extract information from individual locations. Locations, when measured as points, can include things like:

  • Health providers: Hospitals, Clinics, Pharmacies, Mental health providers, Medication for opioid use disorder providers
  • Area resources: Grocery stores & Supermarkets, Playgrounds, Daycare centers, Schools, Community centers
  • Area challenges: Crime, Superfund sites, Pollution-emitting facilities

Points can also represent people, like individual patients residing in an area. Because individual locations for persons is protected health information, we’ll focus on point data as resources in the chapter. However, you can reuse the code snippets in this workshop to wrangle patient-level data the same way in a secure environment, under the guidance of your friendly IRB ethics board.

In this example, we’ll extend our Chicago example. We’ll identify areas with high COVID rates, low geographic access to methadone maintenance therapy, and less access to affordable rental housing units managed by the city. We are interested in locating zip codes that may be especially vulnerable to persons with opioid use disorder who use MOUDs. (This is oversimplified, but our example to work with.)

3.1 Geocode

If you start with only addresses, you’ll need to geocode. Our methadone maintenance provider dataset is only available as such. Addresses are comprised of characeters that reference a specific place. We will use the network topology service of a Geocoder to translate that address to a coordinate in some CRS.

First we load the tidygeocoder to get our geocoding done. Note, this uses the interent to process, so is not suitable for HIPPA protected data like individual, living person addresses. For offline geocoders, check out Pelias or ESRI.

Let’s read in and inspect data for methadone maintenance providers. Note, these addresses were made available by SAMSHA, and are known as publicly available information. An additional analysis could call each service to check on access to medication during COVID in Septmber 2020, and the list would be updated further.

##   X                                                         Name
## 1 1                Chicago Treatment and Counseling Center, Inc.
## 2 2                      Sundace Methadone Treatment Center, LLC
## 3 3 Soft Landing Interventions/DBA Symetria Recovery of Lakeview
## 4 4                                        PDSSC - Chicago, Inc.
## 5 5                          Center for Addictive Problems, Inc.
## 6 6                                Family Guidance Centers, Inc.
##                   Address    City State   Zip
## 1 4453 North Broadway st. Chicago    IL 60640
## 2 4545 North Broadway St. Chicago    IL 60640
## 3    3934 N. Lincoln Ave. Chicago    IL 60613
## 4     2260 N. Elston Ave. Chicago    IL 60614
## 5        609 N. Wells St. Chicago    IL 60654
## 6     310 W. Chicago Ave. Chicago    IL 60654

Let’s geocode one address first, just to make sure our system is working. We’ll use the “cascade” method which use the US Census and OpenStreetMap geocoders. These two services are the main options with tidygeocoder.

## # A tibble: 1 x 4
##   address                         latitude longitude geo_method
##   <chr>                              <dbl>     <dbl> <chr>     
## 1 2260 N. Elston Ave. Chicago, IL     41.9     -87.7 census

As we prepare for geocoding, check out the structure of the dataset. Do we need to change anything? The data should be a character to be read properly.

## 'data.frame':    27 obs. of  6 variables:
##  $ X      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Name   : Factor w/ 25 levels "*","A Rincon Family Services",..: 5 25 23 21 3 8 2 1 14 24 ...
##  $ Address: Factor w/ 27 levels "110 E. 79th St.",..: 20 21 17 6 23 10 16 3 5 8 ...
##  $ City   : Factor w/ 1 level "Chicago": 1 1 1 1 1 1 1 1 1 1 ...
##  $ State  : Factor w/ 1 level "IL": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Zip    : int  60640 60640 60613 60614 60654 60654 60651 60607 60607 60616 ...

We need to clean the data a bit. We’ll add a new column for a full address, as required by the geocoding service. When you use a geocoding service, be sure to read the documentation and understand how the data needs to be formatted for input.

We’re ready to go! Batch geocode with one function, and inspect:

## # A tibble: 6 x 10
##       X Name  Address City  State   Zip fullAdd latitude longitude
##   <int> <fct> <fct>   <fct> <fct> <int> <chr>      <dbl>     <dbl>
## 1     1 Chic… 4453 N… Chic… IL    60640 4453 N…     NA        NA  
## 2     2 Sund… 4545 N… Chic… IL    60640 4545 N…     NA        NA  
## 3     3 Soft… 3934 N… Chic… IL    60613 3934 N…     42.0     -87.7
## 4     4 PDSS… 2260 N… Chic… IL    60614 2260 N…     41.9     -87.7
## 5     5 Cent… 609 N.… Chic… IL    60654 609 N.…     41.9     -87.6
## 6     6 Fami… 310 W.… Chic… IL    60654 310 W.…     41.9     -87.6
## # … with 1 more variable: geo_method <chr>

There were two that didn’t geocode correctly. You can inspect further. This could involve a quick check for spelling issues; or, searching the address and pulling the lat/long using Google Maps and inputting manually. Or, if we are concerned it’s a human or unknown error, we could omit. For this exercise we’ll just omit the two clinics that didn’t geocode correctly.

3.2 Convert to Spatial Data

This is not spatial data yet! To convert a static file to spatial data, we use the powerful st_as_sf function from sf. Indicate the x,y parameters (=longitude, latitude) and the coordinate reference system used. Our geocoding service used the standard EPSG:4326, so we input that here.

3.3 Basic Map of Points

For a really simple map of points – to ensure they were geocoded and converted to spatial data correctly, we use tmap. We’ll use the interactive version to view.

If your points didn’t plot correctly:

  • Did you flip the longitude/latitude values?
  • Did you input the correct CRS?

Those two issues are the most common errors.

3.4 Overlay Points & Style

Let’s add our zip code map from the previous module. First load the data, then overlay.

## Reading layer `ChiZipMaster1' from data source 
##   `/Users/maryniakolak/code/Intro2RSpatialMed/data/ChiZipMaster1.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 540 features and 31 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.87596 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
## CRS:           4326

With this overlay, we’ll add a “hack” to include the methadone clinic points in a legend.

## tmap mode set to plotting

3.5 Integrate More Data

From here, we can integrate more data. Let’s try a different point dataset – Affordable Rental Housing Developments, as made available by the City of Chicago Data Portal. This could be interesting for a number of different reasons – maybe we hypothesize better outcomes are associated with better access to affordable housing options? Or, we hypothesize the opposite, that mean distance to more population dense housing locations is vulnerable to airborne disease?

For this example, we’ll think about this dataset as access to secure and affordable housing. Persons with lower incomes residing in places with fewer developments may be more vulnerable to housing insecurity -> impacts health.

##   Community.Area.Name Community.Area.Number      Property.Type
## 1           Englewood                    68           Veterans
## 2         Rogers Park                     1             Senior
## 3              Uptown                     3                ARO
## 4           Edgewater                    77             Senior
## 5            Roseland                    49 Supportive Housing
## 6       Humboldt Park                    23        Multifamily
##          Property.Name                            Address Zip.Code
## 1   Hope Manor Village 5900-6100 S. Green/Peoria/Sangamon    60621
## 2   Morse Senior Apts.                 6928 N. Wayne Ave.    60626
## 3           The Draper                   5050 N. Broadway    60640
## 4        Pomeroy Apts.               5650 N. Kenmore Ave.    60660
## 5    Wentworth Commons            11045 S. Wentworth Ave.    60628
## 6 Nelson Mandela Apts.                 607 N. Sawyer Ave.    60624
##   Phone.Number             Management.Company Units X.Coordinate
## 1 312-564-2393 Volunteers of America Illinois    36           NA
## 2 312-602-6207               Morse Urban Dev.    44      1165844
## 3 312-818-1722                      Flats LLC    35      1167357
## 4 773-275-7820                Habitat Company   198      1168181
## 5 773-568-7804        Mercy Housing Lakefront    50      1176951
## 6 773-227-6332               Bickerdike Apts.     6      1154640
##   Y.Coordinate Latitude Longitude
## 1           NA       NA        NA
## 2      1946059 42.00757 -87.66517
## 3      1933882 41.97413 -87.65996
## 4      1937918 41.98519 -87.65681
## 5      1831516 41.69302 -87.62777
## 6      1903912 41.89215 -87.70753
##                                Location
## 1                                      
## 2 (42.0075737709331, -87.6651711448293)
## 3 (41.9741295261027, -87.6599553011627)
## 4  (41.9851867755403, -87.656808676983)
## 5 (41.6930159120977, -87.6277673462214)
## 6 (41.8921534052465, -87.7075265659001)

There were a few data points with odd inputs and null values. Remember, we can’t convert any null values to spatial coordinates. Again, in an ideal context, you would explore and understand what is happening, systematically. In our experiment, we’ll omit nulls.

Look at the structure of the object.

## 'data.frame':    487 obs. of  14 variables:
##  $ Community.Area.Name  : Factor w/ 65 levels "Albany Park",..: 49 55 17 50 25 22 65 45 45 37 ...
##  $ Community.Area.Number: int  1 3 77 49 23 38 42 36 36 8 ...
##  $ Property.Type        : Factor w/ 26 levels "65+/Supportive",..: 13 2 13 19 8 8 8 8 13 19 ...
##  $ Property.Name        : Factor w/ 394 levels "1038 N. Ashland",..: 214 338 257 376 218 186 109 235 234 348 ...
##  $ Address              : Factor w/ 478 levels "10 N. Hamlin Ave.",..: 424 348 367 17 384 298 71 279 275 64 ...
##  $ Zip.Code             : int  60626 60640 60660 60628 60624 60653 60637 60653 60653 60622 ...
##  $ Phone.Number         : Factor w/ 326 levels "217-779-5697",..: 57 78 134 221 111 232 225 164 164 32 ...
##  $ Management.Company   : Factor w/ 200 levels "@properties",..: 112 57 66 104 17 80 176 167 169 74 ...
##  $ Units                : int  44 35 198 50 6 71 67 534 148 40 ...
##  $ X.Coordinate         : num  1165844 1167357 1168181 1176951 1154640 ...
##  $ Y.Coordinate         : num  1946059 1933882 1937918 1831516 1903912 ...
##  $ Latitude             : num  42 42 42 41.7 41.9 ...
##  $ Longitude            : num  -87.7 -87.7 -87.7 -87.6 -87.7 ...
##  $ Location             : Factor w/ 477 levels "","(41.648457411436, -87.5401231660406)",..: 475 455 462 8 289 122 67 147 151 328 ...
##  - attr(*, "na.action")= 'omit' Named int 1
##   ..- attr(*, "names")= chr "1"

In this dataset, we can see coordinate information is already included – twice! You’re looking at 2 different types of coordinate systems. We’ll use “Longitude” and “Latitude” to represent X,Y and an ESPG of 4326. We’re guessing, and hopeful.

We can now map the data for a quick view – does this look like Chicago, hopefully?

3.6 Graduated Symbology

Previously we mapped points as dots. We literally used the tm_dots() function to do so. Another option is changing the size of the point, according to some attribute of the data. In this dataset, we see an attribute field that gives us the total number of units per housing site. Let’s use a graduated symbology, with the tm_bubbles() function, to map these points. That way points with more units will be bigger, and not all places are weighted the same visually.

3.7 Style Final Map

Let’s pull what we learned in the last tutorial, and map everything at once. Which zip codes are the most vulnerable to persons with OUD during the pandemic in September 2020, based on the information we have here?

In RStudio, you could zoom into the plot you created to get a better view. Save as an image, or save as a webpage!

Save any data you need from this session.

4 Calculate Spatial Metrics

While we’ve generated some nice visualizations, we need insights quantified as metrics at the neighborhood level. Don’t start this step until you have a good idea of what you need. Visualizing and exploring the data in depth is best practice.

For our purposes, we’re interested in developing spatial access metrics with a container method approach. At the end of this tutorial, we’ll generate the following new variables:

  • Total number of Methadone Maintenance MOUD by zip code
  • Total number of Walkble MOUD Service Areas by zip code

Plus, we will have a new spatial layer, that includes the actual service areas (ie. 1-mile buffers of MOUDs). We assume that access to MOUDs is critical and requires high regularity, and that walking is the most likely option during COVID. This guides the parameter specification of MOUD Service Areas (and is also backed up by some literature in this space, though much more is needed.)

4.1 Load Spatial Data

Let’s first reload our spatial data – this will be the MOUD points, plus the master zip code spatial file.

## Reading layer `methadoneMOUD' from data source 
##   `/Users/maryniakolak/code/Intro2RSpatialMed/data/methadoneMOUD.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 25 features and 8 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -87.7349 ymin: 41.68698 xmax: -87.57673 ymax: 41.9533
## CRS:           4326
## Reading layer `geo_export_54bc15d8-5ef5-40e4-8f72-bb0c6dbac9a5' from data source 
##   `/Users/maryniakolak/code/Intro2RSpatialMed/data/geo_export_54bc15d8-5ef5-40e4-8f72-bb0c6dbac9a5.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 61 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.94011 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
## CRS:           4326
## Simple feature collection with 6 features and 8 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -87.72186 ymin: 41.88474 xmax: -87.63409 ymax: 41.9533
## CRS:           4326
##   X                                                         Name
## 1 3 Soft Landing Interventions/DBA Symetria Recovery of Lakeview
## 2 4                                        PDSSC - Chicago, Inc.
## 3 5                          Center for Addictive Problems, Inc.
## 4 6                                Family Guidance Centers, Inc.
## 5 7                                     A Rincon Family Services
## 6 8                                                            *
##                Address    City State   Zip
## 1 3934 N. Lincoln Ave. Chicago    IL 60613
## 2  2260 N. Elston Ave. Chicago    IL 60614
## 3     609 N. Wells St. Chicago    IL 60654
## 4  310 W. Chicago Ave. Chicago    IL 60654
## 5   3809 W. Grand Ave. Chicago    IL 60651
## 6  140 N. Ashland Ave. Chicago    IL 60607
##                                 fullAdd geo_method
## 1 3934 N. Lincoln Ave. Chicago IL 60613     census
## 2  2260 N. Elston Ave. Chicago IL 60614     census
## 3     609 N. Wells St. Chicago IL 60654     census
## 4  310 W. Chicago Ave. Chicago IL 60654     census
## 5   3809 W. Grand Ave. Chicago IL 60651     census
## 6  140 N. Ashland Ave. Chicago IL 60607        osm
##                     geometry
## 1  POINT (-87.67818 41.9533)
## 2 POINT (-87.67407 41.92269)
## 3 POINT (-87.63409 41.89268)
## 4 POINT (-87.63636 41.89657)
## 5 POINT (-87.72186 41.90435)
## 6 POINT (-87.66725 41.88474)
## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -87.80649 ymin: 41.88747 xmax: -87.59852 ymax: 41.93228
## CRS:           4326
##   objectid shape_area shape_len   zip                       geometry
## 1       33  106052287  42720.04 60647 MULTIPOLYGON (((-87.67762 4...
## 2       34  127476051  48103.78 60639 MULTIPOLYGON (((-87.72683 4...
## 3       35   45069038  27288.61 60707 MULTIPOLYGON (((-87.785 41....
## 4       36   70853834  42527.99 60622 MULTIPOLYGON (((-87.66707 4...
## 5       37   99039621  47970.14 60651 MULTIPOLYGON (((-87.70656 4...
## 6       38   23506056  34689.35 60611 MULTIPOLYGON (((-87.61401 4...

4.2 Transform Projections

First we need to switch to a projection that uses distance in feet or meters as a metric. We’ll use EPSG:3435 from the first tutorial. To find which EPSG was recommended, I searched “EPSG Illinois feet” and EPSG:3435 came up as a viable candidate. So, we use that for our new, projected CRS.

We may want to once again confirm they are plotting correctly:

4.3 Count resources by area

One way of understanding resource inequity is by thinking about how many resources exist in a neighborhood.

First, give the points the attributes of the polygons they are in. Inspect.

## Simple feature collection with 6 features and 12 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 1150707 ymin: 1901294 xmax: 1174632 ymax: 1926255
## CRS:           EPSG:3435
##   X                                                         Name
## 1 3 Soft Landing Interventions/DBA Symetria Recovery of Lakeview
## 2 4                                        PDSSC - Chicago, Inc.
## 3 5                          Center for Addictive Problems, Inc.
## 4 6                                Family Guidance Centers, Inc.
## 5 7                                     A Rincon Family Services
## 6 8                                                            *
##                Address    City State   Zip
## 1 3934 N. Lincoln Ave. Chicago    IL 60613
## 2  2260 N. Elston Ave. Chicago    IL 60614
## 3     609 N. Wells St. Chicago    IL 60654
## 4  310 W. Chicago Ave. Chicago    IL 60654
## 5   3809 W. Grand Ave. Chicago    IL 60651
## 6  140 N. Ashland Ave. Chicago    IL 60607
##                                 fullAdd geo_method objectid shape_area
## 1 3934 N. Lincoln Ave. Chicago IL 60613     census       53   53990895
## 2  2260 N. Elston Ave. Chicago IL 60614     census       32   94460632
## 3     609 N. Wells St. Chicago IL 60654     census       55   15869961
## 4  310 W. Chicago Ave. Chicago IL 60654     census       54   31598157
## 5   3809 W. Grand Ave. Chicago IL 60651     census       37   99039621
## 6  140 N. Ashland Ave. Chicago IL 60607        osm       16  106718949
##   shape_len   zip                geometry
## 1  31196.32 60613 POINT (1162460 1926255)
## 2  50587.35 60614 POINT (1163663 1915110)
## 3  17119.70 60654 POINT (1174632 1904257)
## 4  24208.70 60610 POINT (1174003 1905671)
## 5  47970.14 60651 POINT (1150707 1908328)
## 6  42663.20 60612 POINT (1165627 1901294)

You should have the same number of rows in pipr as you do in points. If not, there is something off. You may need to go back to troubleshoot. In an earlier version of this lab, I used a saved, written geojson file of the zip codes, and it would not render properly. Here, we load in the original shapefile at the beginning of the tutorial to avoid that error.

## [1] 25 13
## [1] 25  9
## [1] 61  5

4.3.1 Count # per Area

Next, count the number per area. The frequency should be logical according to the map you made earlier. Sometimes, I’ve found bugs where the numbers are multipled by some factor; this can be checked by looking at dimension disparities, as noted above.

##    Var1 Freq
## 1 60607    2
## 2 60608    1
## 3 60609    1
## 4 60613    1
## 5 60614    1
## 6 60615    1

How could improve on this step if you used dplyr?

Aggregation Tip: What if you have an attribute value you’d like to aggregate? For example, average units of affordable housing facility by zip? Try aggregate(pip$attribute, by = list(pip$geoid), mean) but build on with a tidy sensibility…

Now we can rename our attributes:

##     zip MetClnc
## 1 60607       2
## 2 60608       1
## 3 60609       1
## 4 60613       1
## 5 60614       1
## 6 60615       1

And finally, merge back to your master zip file:

## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1127607 ymin: 1902374 xmax: 1184320 ymax: 1918596
## CRS:           EPSG:3435
##   objectid shape_area shape_len   zip                       geometry
## 1       33  106052287  42720.04 60647 MULTIPOLYGON (((1162711 191...
## 2       34  127476051  48103.78 60639 MULTIPOLYGON (((1149304 191...
## 3       35   45069038  27288.61 60707 MULTIPOLYGON (((1133505 190...
## 4       36   70853834  42527.99 60622 MULTIPOLYGON (((1165664 190...
## 5       37   99039621  47970.14 60651 MULTIPOLYGON (((1154895 190...
## 6       38   23506056  34689.35 60611 MULTIPOLYGON (((1180097 190...
## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1173038 ymin: 1889918 xmax: 1183259 ymax: 1902959
## CRS:           EPSG:3435
##     zip objectid shape_area shape_len MetClnc
## 1 60601       27    9166246  19804.58      NA
## 2 60602       26    4847125  14448.17      NA
## 3 60603       19    4560229  13672.68      NA
## 4 60604       48    4294902  12245.81      NA
## 5 60605       20   36301276  37973.35      NA
## 6 60606       31    6766411  12040.44      NA
##                         geometry
## 1 MULTIPOLYGON (((1177742 190...
## 2 MULTIPOLYGON (((1181226 190...
## 3 MULTIPOLYGON (((1179499 190...
## 4 MULTIPOLYGON (((1174763 189...
## 5 MULTIPOLYGON (((1178341 189...
## 6 MULTIPOLYGON (((1174681 190...

Quickly map to inspect:

4.4 Buffer Data

Next, lets create a walkable buffer of one mile, or 5280 feet, for our MOUD provider locations. Individuals residing in places outside of that walkabile area may have difficulty accessing this medication during crises, like a pandemic.

Inspect immediately:

4.5 Count buffers by area

We know that MOUD locations are accessible up to one mile away. So, a total count of resources by area may be too restrictive. Let’s calculate how many walkable MOUD clinics are in each zip code. Or, how many buffers are in each area…

## [1] 2 2 1 1 1 2

Stick buffer totals back to the zip master file:

## Simple feature collection with 6 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1173038 ymin: 1889918 xmax: 1183259 ymax: 1902959
## CRS:           EPSG:3435
##     zip objectid shape_area shape_len MetClnc bufferct
## 1 60601       27    9166246  19804.58      NA        2
## 2 60602       26    4847125  14448.17      NA        2
## 3 60603       19    4560229  13672.68      NA        1
## 4 60604       48    4294902  12245.81      NA        1
## 5 60605       20   36301276  37973.35      NA        1
## 6 60606       31    6766411  12040.44      NA        2
##                         geometry
## 1 MULTIPOLYGON (((1177742 190...
## 2 MULTIPOLYGON (((1181226 190...
## 3 MULTIPOLYGON (((1179499 190...
## 4 MULTIPOLYGON (((1174763 189...
## 5 MULTIPOLYGON (((1178341 189...
## 6 MULTIPOLYGON (((1174681 190...

Map density of buffers per census area:

4.6 Integrate & Explore

Let’s review: our master area file now has total number resources by zip and total number of walkable service areas by zip.

Using your new spatial file, see if you can answer some of these quetions using various queries:

  • Which zip codes have high rates of COVID and are not within a walking distance of a methadone MOUD?

  • Which zip codes have worse access to affordable rental units, low educational rates, and less walkable access to MOUDs?

  • What is the demographic and racial/ethnic characteristics of areas most vulnerable to high COVID rates in September 2020?

Generate different maps and outputs to drive your thinking and defend your hypothesis formation.